Linear Predictive Coding Model Research Articles

Performance improvement of the Goertzel algorithm in estimating of protein coding regions using modified anti-notch filter and linear predictive coding model

The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HMR195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm.

THE EFFECT OF USING HAMMING WINDOW AND LINEAR PREDICTIVE CODING MODEL IN EEG-P300 SIGNALS CLASSIFICATION

In this paper, Fisher linear discriminant analysis (FLDA) is used to classify the EEGP-300 signals which are extracted from brain activities. In this case, at first the preprocessing algorithms such as filtering and referencing are applied to the raw EEG signal. Then, in order to create a model out of the signal, a linear predictive coding model with 6 order is used. So that the signal is reconstructed by extracting linear predictive coding (LPC) model parameters of each single trial, and then every signal trial is passed through the Hamming window by length 9. At last Fisher Linear Discriminant Analysis is used for classifying. In this paper, classification accuracy, the maximum bit rate and the convergence time to achieve stability in maximum accuracy of classification are computed to compare performance of the proposed method, Fisher Linear Discriminant Analysis with Linear Predictive Coding Model and Hamming Window (LPC+HAMMING+FLDA), to FLDA and LPC+FLDA. The implementation results show that the efficiency of the proposed method in terms of classification accuracy and convergence time to achieve stability in maximum accuracy is better than the other two mentioned algorithms. As example, at the proposed algorithm with 8 electrode configuration the S2 converges to the maximum accuracy after eleventh Block while this happens for two other algorithms after fourteenth Block and the total classification accuracy for this person at proposed algorithm is improved as 2.2% and 4% than respectively LPC+FLDA and FLDA algorithms.

Brain Activity EEG-P300 Signal Categorization from LPC based Estimation of Signal using Fisher Linear Discriminant Analysis

In this paper, a new approach is proposed to categorize the EEG-P300 signals of brain activity using fisher linear discriminant analysis (FLDA) based on smoothed signal achieved from linear predictive coding (LPC) filter. In order to overcome the overtraining of classifier caused by the noisy and non-stationary data, EEG signal is reconstructed using LPC model before applying to FLDA. Since, the brain signals are noisy, LPC model can highlight the signal features and reduce the irrelevant information. In this paper, classification accuracy, maximum bitrate and speed to achieve stability in maximum accuracy of classification are computed to compare performance of the proposed algorithm (LPC-FLDA) and FLDA. The implementation results show that the efficiency of the proposed method in terms of classification accuracy and convergence time to achieve stability in maximum accuracy is better than FLDA algorithm. As example at the proposed algorithm with 8 electrode configuration the S4 converges to the maximum accuracy after tenth Block while this happens for FLDA algorithm after thirteenth Block and the total classification accuracy for this person at proposed algorithm is improved as 0.8% than FLDA algorithm.

A novel hierarchical decomposition vector quantization method for high-order LPC parameters

The paper investigates vector quantization coding of high-order (e.g., 20th-50th order) linear prediction coding (LPC) parameters, and proposes a novel hierarchical decomposition vector quantization method for a scalable speech coding framework with variable orders of LPC analysis. Instead of vector quantizing the whole group of LPC parameters in the linear spectral frequency (LSF) domain directly, the proposed method decomposes the high-order LPC model into several low-order (e.g., 10th-order) LPC models, and vector quantizes them in the LSF domain separately. For the decomposition, the high-order LPC model is converted into a group of reflection coefficients at first, and then the group is split into several subgroups and converted into multiple low-order LPC models. It is shown that the proposed method is naturally suitable for a scalable coding framework where the information of the decomposed low-order LPC models can be encoded into a multi-layered bitstream and can be combined in a progressive way to recover the high-order LPC information. Experiments in a scalable coding framework with variable LPC analysis orders (10-50) reveal that, compared to a direct vector quantization scheme, the proposed method can reduce the size of the codebook and the number of coding bits significantly, and can also efficiently reduce the computation cost.

A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve

identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3- base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, first, the symbolic DNA sequences are converted to digital signal using the Z-curve method, which is a unique 3-D plot to illustrate DNA sequence and presents the biological behavior of DNA sequence. Then a novel fast algorithm is proposed to investigate the location of exons in DNA strand based on the combination of Linear Predictive Coding Model (LPCM) and Goertzel algorithm. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificity - sensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction. In this paper, we have also developed a useful user friendly package to analyze DNA sequences. Keywordssequence; Protein coding regions; Signal processing; Exon; Linear predictive coding model; Goertzel algorithm.

ROOM ACOUSTIC RESPONSE MODELING AND EQUALIZATION WITH LINEAR PREDICTIVE CODING AND PARAMETRIC FILTERS

A method for determining coefficients of a family of cascaded second order Infinite Impulse Response (IIR) parametric filters used for equalizing a room response. The method includes determining parameters of each IIR parametric filter from poles or roots of a reasonably high-order Linear Predictive Coding (LPC) model. The LPC model is able to accurately model the low-frequency room response modes providing better equalization of loudspeaker and room acoustics, particularly at the low frequencies. Advantages of the method include fast and efficient computation of the LPC model using a Levinson-Durbin recursion to solve the normal equations that arise from the least squares formulation. Due to possible band interactions between the cascaded IIR parametric filters, the method further includes optimizing the Q value of each filter to better equalize the room response.

Reconciling Predictive Coding and Biased Competition Models of Cortical Function

A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model.

Effect of White-Noise Correction on Linear Predictive Coding

White-noise correction is a technique used in speech coders using linear predictive coding (LPC). This technique generates an artificial noise-floor in order to avoid stability problems caused by numerical round-off errors. In this letter, we study the effect of white-noise correction on the roots of the LPC model. The results demonstrate in analytic form the relation between the noise floor level and the stability radius of the LPC model

Successive bit‐vector search algorithm for celp vocoders

In this paper, we propose a successive bit‐vector search approach to reduce the computational complexity of coded excited linear predictive coding (CELP) speech coders. By decomposing each codevector into several bit‐vectors, the convolution process of the linear predictive coding model can be simplified. With a suggested matching criterion, the proposed method removes a portion of unlikely codevectors in a bit‐vector by bit‐vector fashion. Theoretical analyses and simulation results show that the proposed search method reduces a lot of the computational load for general codebook searches and maintains nearly the same speech quality as the traditional search approach. The proposed method, which reduces the complexity of the CELP speech coders in VLSI realization, can be flexibly embedded with a simple RISC processor to achieve a low‐cost speech coder.

LPC spectral moments for clustering acoustic transients

Spectral moments (mean and coefficients of variation, skewness, and kurtosis) are assessed for 40 samples from 10 groups of acoustic transient signals differing in harmonic structure, duration, and degree of spectral overlap. Discriminant analysis involving moments based on linear predictive coding (LPC) resulted in a higher recognition rate for pulsed-tone sounds (87%) that were more like human speech than for pure-tone sounds (70%). By contrast, classification based on moments calculated from the discrete Fourier transform (DFT) yielded 85% recognition for both groups. Cluster analyses indicated that LPC-based moments were more characteristic of relationships among the 10 sound groups and especially the two tonal groups, though results were somewhat dependent on LPC model order. >

On 450-600 b/s natural sounding speech coding

Algorithms for encoding speech with good intelligence and naturalness at very low rates are studied. Naturalness is retained by encoding accurately the speech excitation information from an LPC (linear predictive coding) model. A glottal ARX (autoregressive with exogenous input) technique is used to model the speech signal for high quality. A large reduction in coding rate is achieved through short-term temporal compression of the speech and vector quantization. Application of traditional vector quantization to the temporal decomposition output is discussed, with consideration of distortion measures and codebook generation. Based on properties of short-term temporal decomposition, finite-state vector quantization is introduced to further decrease the coding rate. A problem associated with this technique, estimation of a state transition matrix with incomplete data, is treated. The general result is that practical coders operating in a range of 450-600 b/s with a delay of about 200 ms and natural-sounding output speech can be designed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An approach to parameter reoptimization in multipulse-based coders

An algorithm for LPC parameter optimization in multipulse (MP)-linear-prediction-coding (LPC)-based speech coders is presented. It is shown that by taking the nature of the MP excitation signal into account in the LPC parameter computation, it is possible to improve the effectiveness of the LPC model. This results in a better quality of the reconstructed signal in terms of both objective and subjective criteria. The implementation details of the algorithm are discussed, and experimental results are presented. In particular, a comparison with standard MP LPC techniques is given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Unstable covariance LPC solutions from nonstationary speech waveforms

Linear Predictive Coding (LPC) analysis of speech is made using a stationary model while parts of speech such as stop consonants are highly nonstationary. An asymptotic analysis is made of the stability of the LPC model obtained from a simplified model of a nonstationary waveform. This model is used to predict the occurrence of unstable LPC models in the analysis of a stop consonant. >

NNet: A natural voice neural network speech synthesizer

Reproduced speech extracted from actual voice recordings of a few selected individuals representing a wide variance of age, speech dialect (English), and sex was used as input values to a four‐layer (input, hidden, output, and state) generalized backward propagation artificial neural network system (ANS) using a linear prediction coding (LPC) model to produce more natural sounding artificial speech. The speech phonetic research environment (SPIRE) MIT‐developed software package running on a Symbolics computer was utilized in making the original voice recordings. The linear predictive analysis of the LPC model efficiently represented the speech signals in terms of slowly varying parameters. It converted the combined spectral contributions of the glottal flow within a pitch period, the vocal tract, and the radiation of the lips into a single recursive (all‐pole) time‐varying filter. The transfer function of the filter involved the use of gain coefficients that, when fed into the system, were found to destabilize it. The LPC model was modified to use impulse characteristics of the system rather than the individual discrete coefficients. Preliminary data indicated close output to input matches, artificial speech to natural speech, indicating the ability of the artificial neural network to learn the timing, pitch fluctuations, and connectivity between individual sounds, and the speaking habits unique to a person.

Rate-distortion bounds for quotient-based distortions with application to Itakura- Saito distortion measures

In many linear predictive coding (LPC) speech compression systems the encoding of the LPC parameters is performed using a product code book scheme. One possible approach consists in organizing the set of LPC reproduction models as the Cartesian product of a vector code book describing the shape of each reproduction LPC model and a scalar codebook describing the gain or energy. We first present a formal development of rate-distortion theoretic results for distortion functions based on quotients of input and reproduction symbols. We also obtain theoretical bounds for the rate-distortion function of the gain term of LPC systems (which, depending on the quantization scheme, can use about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10-25</tex> percent of the transmission rate), as well as asymptotic (small distortions) approximations to the performance of the optimal scalar quantizer for these gain terms, when the overall fidelity criterion is the Itakura-Saito distortion measure, which is a member of the class of quotient distortion measures. The approximations and bounds are compared finally with experimental results.

Peculiarities of Wiener Class Systems and their Exploitation for Speech Signal Prediction

In this paper Wiener class system consisting of infinite impulse response dynamic subsystem and sigmoid nonlinearity is employed for speech modelling. The experiments on Lithuanian vowel "ū" with different initial phase of the signal are done. The average of mean square errors of prediction with the Wiener system model are smaller approximately by 18 % for the training dataset, 17 % for the validation dataset and 14 % for the testing dataset in comparison to Linear Prediction Coding model. Additional experiments with 6 different Lithuanian vowels on average showed more than 13 % improvement in MSE of prediction with the Wiener system model. Moreover, the use of Wiener system instead of IIR model showed improvement in MSE of long-term prediction for five Lithuanian vowels on average by more than 5 %. Application of results in telecommunication systems could possibly decrease data transfer rates, increase speech intelligibility and naturalness of speech synthesis. Ill. 5, bibl. 8, tabl. 2 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.368